基于级联卷积神经网络的荧光免疫层析图像峰值点定位方法研究

针对目前荧光免疫层析定量图像峰值点定位易受多种因素影响,导致物质定量准确度低的问题,提出了一种融合目标检测的级联卷积神经网络(CNN)算法。第一层级联算法首先使用经改进的AlexNet算法对荧光免疫层析定量图像中包含质控(C)峰和检测(T)峰的区域进行检测和提取。之后将提取到的图像区域送入第二层级联卷积神经网络中,对C峰和T峰的位置进行快速定位。随后将定位结果输入到第三层级联卷积神经网络中,对上一层输出的C峰和T峰的定位结果进行精准微调。最后输出C峰和T峰的准确定位信息。实验结果表明,提出的级联卷积神经网络算法,对荧光免疫层析图像峰值点的平均定位准确度达到了96%以上,提高了峰值点的定位准确度。

Peak point location of fluorescence immunochromatography image basedon the cascaded convolutional neural network

The peak point location is susceptible to many factors of the fluorescence immunochromatographic quantitative image, which can cause the problem of low substance quantification accuracy. To address this issue, a cascaded convolutional neural network(CNN) algorithm for fusion target detection is proposed. The improved AlexNet is utilized in the first-level cascade algorithm to detect and extract the regions containing the quality control(C) peak and test(T) peak in the fluorescence immunochromatographic quantitative image. The extracted image area is sent to the second-level cascaded convolutional neural network to locate C peak and T peak quickly. Then, the location results are taken as the input of the third-level cascaded convolutional neural network. The fine-tune the location results of the C peak and T peak can be realized from the previous layer. Finally, the accurate location information of the C peak and T peak is achieved. Experimental results show that the proposed cascaded convolutional neural network algorithm can locate the peak points of fluorescence immunochromatography images with the accuracy of more than 96%, and the location accuracy of peak points is enhanced.